Battery Model Parameter Identification and SOC Estimation Considering Voltage Measurement Noise

被引：0
|
作者：
Jiang B. [1 ]
Dai H. [1 ]
Wei X. [1 ]
Xu T. [2 ]
机构：
[1] School of Automotive Studies, Tongji University, Shanghai
[2] United Automotive Electronic Systems Co., Ltd., Shanghai
来源：
Tongji Daxue Xuebao/Journal of Tongji University | 2018年 / 46卷
关键词：
Parameter identification; Recursive compensated least squares (RCLS); State of charge (SOC); Voltage measurement noise;
D O I：
10.11908/j.issn.0253-374x.2018.s1.032
中图分类号：
学科分类号：
摘要：
This paper presents a method for parameter identification based on the recursive compensated least squares (RCLS) algorithm, which provides accurate battery model parameters for model-based battery state of charge (SOC) estimation algorithm. The experimental results indicate that, under the interference of voltage measurement noise, the model parameters identified by RCLS algorithm can converge to exact values, and this method is suitable for the battery under different temperatures or different aging states; meanwhile, the extended Kalman filter combined with RCLS algorithm is a relatively high precision and time-saving method for battery SOC estimation. © 2018, Editorial Department of Journal of Tongji University. All right reserved.
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页码：190 / 195
页数：5
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